App. Res. of Comp. Accepted Paper
Vol. 43, 2026 No. 11

Improved SMOTE algorithm based on adaptive extraction of data backbone

Zhu Tao1a,2
Gao Guangliang1b
Xia Lingling1c,2
Liang Guangjun1c,2
1. a. School of Data and Intelligent Policing Technology, b. School of National Security, c. School of Cyber Science and Engineering, Jiangsu Police Institute, Nanjing Jiangsu 210031, China
2. Jiangsu Electronic Data Forensics and Analysis Engineering Research Center, Nanjing Jiangsu 210031, China
DOI: 10.19734/j.issn.1001-3695.2026.04.0056

Abstract

Although widely used for addressing data imbalance problems, the Synthetic Minority Oversampling Technique (SMOTE) faces challenges such as insufficient data space utilization, limited diversity of synthetic samples, and the tendency to generate noisy samples. Leveraging the characteristic that classifiable datasets tend to cluster and form the backbone of data in their feature space, this paper proposed a BackBoneSMOTE oversampling algorithm based on class distance features and hyperspherical neighborhood density-connectable ideas. This algorithm first defined class backbone points based on the number and density of data points in the hypersphere neighborhood of sample points and proposed an adaptive extraction algorithm that utilizes distance features to filter the set of class backbone points. Then, drawing on the density-connectable idea in density clustering methods, it defined intra-class hypersphere neighborhood density-connectable concept. Based on the backbone and density core characteristics of sample points, it proposed a method for constructing an intra-class data connectivity graph based on the hypersphere neighborhood. Utilizing the hierarchical connectivity characteristics formed by the intra-class data connectivity graph, it adjusted the random linear interpolation in SMOTE algorithm. Using common classifiers on one synthetic and five real imbalanced datasets, extensive experimental results demonstrate that the proposed BackBoneSMOTE algorithm outperforms the common SMOTE algorithm in imbalanced data classification and effectively enhances the classification performance of minority class data.

Foundation Support

国家自然科学基金资助项目(72401110)
江苏省教育科学"十四五"规划课题(C-c/2021/01/11)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2026.04.0056
Publish at: Application Research of Computers Accepted Paper, Vol. 43, 2026 No. 11

Publish History

[2026-07-23] Accepted Paper

Cite This Article

朱涛, 高光亮, 夏玲玲, 等. 基于数据骨干自适应提取的改进SMOTE算法 [J]. 计算机应用研究, 2026, 43 (11). (2026-07-31). https://doi.org/10.19734/j.issn.1001-3695.2026.04.0056. (Zhu Tao, Gao Guangliang, Xia Lingling, et al. Improved SMOTE algorithm based on adaptive extraction of data backbone [J]. Application Research of Computers, 2026, 43 (11). (2026-07-31). https://doi.org/10.19734/j.issn.1001-3695.2026.04.0056. )

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  • Application Research of Computers Monthly Journal
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Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

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